{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 处理思路\n",
    "\n",
    "1. 把预测出来的掩膜图片进行合并\n",
    "2. 对2015年的图片进行闭操作尽量联结，对2017年的图片进行开操作，减少噪声\n",
    "3. 相减后取大于0的数，也就是新增的地上建筑物，置为1\n",
    "4. 对新增建筑的标注图片进行开操作减少噪声\n",
    "5. 加上官方提供的tinysample提高准确率"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型跑出来的结果\n",
    "第一张图是4通道的原图，第二张图是模型运行出来的结果掩膜"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import tifffile as tiff\n",
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "# %matplotlib inline\n",
    "\n",
    "# img_2015_0_0_256 = tiff.imread('input/result_image/2015_0_0_256.tif')\n",
    "\n",
    "# pred_2015_0_9_256 = tiff.imread('output/result_pred/2015_0_0_256.tif')\n",
    "\n",
    "# plt.figure(figsize=(18, 18))\n",
    "# plt.subplot(121)\n",
    "# plt.imshow(img_2015_0_0_256)\n",
    "# plt.subplot(122)\n",
    "# plt.imshow(pred_2015_0_9_256)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对小图片进行开操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import tifffile as tiff\n",
    "# import cv2\n",
    "# import numpy as np\n",
    "# import os\n",
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "# path = 'output/result_pred'\n",
    "# path_save_o = 'output/result_pred_o'\n",
    "# if not os.path.exists(path_save_o):\n",
    "#     os.makedirs(path_save_o)\n",
    "# image_list = create_file_lists_from_path(path, pattern='*.tif')\n",
    "# for dic in image_list:\n",
    "#     image = tiff.imread(dic['file'])\n",
    "#     filename = dic['filename']\n",
    "#     open_kernel = np.ones((10, 10), dtype=np.uint8)\n",
    "#     image_open = cv2.morphologyEx(np.array(image), cv2.MORPH_OPEN, open_kernel)\n",
    "#     ss = os.path.join(path_save_o, filename + '.tif')\n",
    "#     cv2.imwrite(ss, image_open)\n",
    "\n",
    "# print(ss)\n",
    "\n",
    "# plt.figure(figsize=(10, 10))\n",
    "# plt.subplot(121)\n",
    "# plt.imshow(image)\n",
    "# plt.subplot(122)\n",
    "# plt.imshow(image_open)\n",
    "# plt.show()\n",
    "# print(image_open.shape)\n",
    "\n",
    "# print('Open completed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 比较15和17的图片，生成结果-新建建筑的识别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import tifffile as tiff\n",
    "# import os\n",
    "# import numpy as np\n",
    "# import cv2\n",
    "\n",
    "# path = 'output/result_pred_o'\n",
    "# pattern_2015 = '2015*.tif'\n",
    "# pattern_2017 = '2017*.tif'\n",
    "# path_save = 'output/result_pred_compare'\n",
    "# if not os.path.exists(path_save):\n",
    "#     os.makedirs(path_save)\n",
    "\n",
    "# img_list_2015 = create_file_lists_from_path(path, pattern=pattern_2015)\n",
    "\n",
    "# for img in img_list_2015:\n",
    "#     image_2015 = tiff.imread(img['file'])\n",
    "#     filename_2017 = '2017_' + '_'.join(img['filename'].split('_')[1:])\n",
    "#     path_2017 = os.path.join(path, filename_2017+'.tif')\n",
    "#     image_2017 = tiff.imread(path_2017)\n",
    "#     image_batch_last = np.array((image_2017-image_2015)>0, dtype=np.uint8)\n",
    "#     ss = os.path.join(path_save, '_'.join(img['filename'].split('_')[1:]) + '.tiff')\n",
    "#     cv2.imwrite(ss, image_batch_last)\n",
    "\n",
    "# print('Generate completed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 模型预测地上建筑物的完整图片合并\n",
    "\n",
    "把预测出来的大小256的掩膜图片进行合并\n",
    "\n",
    "预测结果图片的路径：output/result_pred\n",
    "\n",
    "保存路径：\n",
    "\n",
    "- output/tiny_15.tif\n",
    "- output/tiny_15.tif\n",
    "\n",
    "变量名：img_tiny_15, img_tiny_17"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 显示15、17pred图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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X431MPkS5chSXM/qiuQw6OfghB+Fg4zNPpseVO7mTpluT08khMzawc+f5l8QlbOj231FW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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6636b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tifffile as tiff\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "img_pred_15 = tiff.imread('output/tiny_15.tif')\n",
    "img_pred_17 = tiff.imread('output/tiny_17.tif')\n",
    "\n",
    "# 查看两年模型预测地上建筑的结果\n",
    "plt.figure(figsize=(20, 20))\n",
    "plt.subplot(121)\n",
    "plt.imshow(img_pred_15)\n",
    "plt.subplot(122)\n",
    "plt.imshow(img_pred_17)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.280679746458\n"
     ]
    }
   ],
   "source": [
    "print(img_pred_15.sum().astype(float)/255/img_pred_15.shape[0]/img_pred_15.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0 255]\n"
     ]
    }
   ],
   "source": [
    "print(np.unique(img_pred_15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 显示15、17河道图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# img_pred_15 = tiff.imread('output/river_15.tif')\n",
    "# img_pred_17 = tiff.imread('output/river_17.tif')\n",
    "\n",
    "# # 查看两年模型预测地上建筑的结果\n",
    "# plt.figure(figsize=(20, 20))\n",
    "# plt.subplot(121)\n",
    "# plt.imshow(img_pred_15)\n",
    "# plt.subplot(122)\n",
    "# plt.imshow(img_pred_17)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# _,contours, hierarchy = cv2.findContours(img_pred_17,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) \n",
    "\n",
    "# cv2.drawContours(img_pred_17,contours,-1,(0,0,255),3) \n",
    "\n",
    "# # 查看两年模型预测地上建筑的结果\n",
    "# plt.figure(figsize=(20, 20))\n",
    "# plt.subplot(121)\n",
    "# plt.imshow(img_pred_15)\n",
    "# plt.subplot(122)\n",
    "# plt.imshow(img_pred_17)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 对2015年的图片进行闭操作，对2017年的图片进行开操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import numpy as np\n",
    "# import tifffile as tiff\n",
    "# import cv2\n",
    "\n",
    "# # path setting\n",
    "# path_15 = 'output/tiny_15.tif'\n",
    "# path_17 = 'output/tiny_17.tif'\n",
    "# path_save_15 = 'output/tiny_close_15.tif'\n",
    "# path_save_17 = 'output/tiny_open_17.tif'\n",
    "\n",
    "# # load images\n",
    "# img_15 = tiff.imread(path_15)\n",
    "# img_17 = tiff.imread(path_17)\n",
    "\n",
    "# # 开闭操作\n",
    "# close_kernel = np.ones((20, 20), dtype=np.uint8)\n",
    "# img_close_15 = cv2.morphologyEx(np.array(img_15), cv2.MORPH_CLOSE, close_kernel)\n",
    "\n",
    "# open_kernel = np.ones((10, 10), dtype=np.uint8)\n",
    "# img_open_17 = cv2.morphologyEx(np.array(img_17), cv2.MORPH_OPEN, open_kernel)\n",
    "\n",
    "# # 格式转换和图片保存\n",
    "# img_close_15 = np.array(img_close_15, np.uint8)\n",
    "# tiff.imsave(path_save_15, img_close_15)\n",
    "# img_open_17 = np.array(img_open_17, np.uint8)\n",
    "# tiff.imsave(path_save_17, img_open_17)\n",
    "\n",
    "# # 查看两年模型预测地上建筑开闭操作后的结果\n",
    "# plt.figure(figsize=(20, 20))\n",
    "# plt.subplot(121)\n",
    "# plt.imshow(img_close_15)\n",
    "# plt.subplot(122)\n",
    "# plt.imshow(img_open_17)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import numpy as np\n",
    "# import tifffile as tiff\n",
    "# import os\n",
    "# import matplotlib.pyplot as plt\n",
    "# %matplotlib inline\n",
    "\n",
    "# IM_ROWS = 5106\n",
    "# IM_COLS = 15106\n",
    "# IMAGE_SIZE = 256\n",
    "\n",
    "# path = 'output/result_pred' # 模型输出的图片\n",
    "# path_save = 'output'\n",
    "\n",
    "# im_tiny = np.zeros((IM_ROWS, IM_COLS))\n",
    "\n",
    "# for i in range(int(IM_ROWS // IMAGE_SIZE)):\n",
    "#     for j in range(int(IM_COLS // IMAGE_SIZE)):\n",
    "#         ss = os.path.join(path, '2017_{}_{}_{}.tif'.format(i, j, IMAGE_SIZE)) # 模型输出的图片合并\n",
    "#         image = tiff.imread(ss)\n",
    "#         im_tiny[i*IMAGE_SIZE:(i+1)*IMAGE_SIZE, j*IMAGE_SIZE:(j+1)*IMAGE_SIZE] = image\n",
    "\n",
    "# # 填补行边角图片\n",
    "# if IM_ROWS%IMAGE_SIZE != 0:\n",
    "#     i = i+1\n",
    "#     for k in range(int(IM_COLS // IMAGE_SIZE)):\n",
    "#         ss = os.path.join(path, '2017_{}_{}_{}.tif'.format(i, k, IMAGE_SIZE)) # 模型输出的图片合并\n",
    "#         image = tiff.imread(ss)\n",
    "#         im_tiny[i*IMAGE_SIZE:IM_ROWS, k*IMAGE_SIZE:(k+1)*IMAGE_SIZE] = image[IMAGE_SIZE-(IM_ROWS-i*IMAGE_SIZE):, :]\n",
    "\n",
    "# # 填补列边角图片\n",
    "# if IM_COLS%IMAGE_SIZE != 0:\n",
    "#     j = j+1\n",
    "#     for k in range(int(IM_ROWS // IMAGE_SIZE)):\n",
    "#         ss = os.path.join(path, '2017_{}_{}_{}.tif'.format(k, j, IMAGE_SIZE)) # 模型输出的图片合并\n",
    "#         image = tiff.imread(ss)\n",
    "#         im_tiny[k*IMAGE_SIZE:(k+1)*IMAGE_SIZE, j*IMAGE_SIZE:IM_COLS] = image[:, IMAGE_SIZE-(IM_COLS-j*IMAGE_SIZE):]\n",
    "        \n",
    "# if IM_ROWS%IMAGE_SIZE != 0 and IM_ROWS%IMAGE_SIZE != 0:\n",
    "#     ss = os.path.join(path, '2017_{}_{}_{}.tif'.format(i, j, IMAGE_SIZE)) # 模型输出的图片合并\n",
    "#     image = tiff.imread(ss)\n",
    "#     im_tiny[i*IMAGE_SIZE:IM_ROWS, j*IMAGE_SIZE:IM_COLS] = image[IMAGE_SIZE-(IM_ROWS-i*IMAGE_SIZE):, IMAGE_SIZE-(IM_COLS-j*IMAGE_SIZE):]\n",
    "\n",
    "# img_tiny_17 = np.array(im_tiny, np.uint8)\n",
    "\n",
    "# plt.figure(figsize=(20, 20))\n",
    "# plt.imshow(img_tiny_17)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. 相减后取大于0的数，也就是新增的地上建筑物，置为1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zRPgil9E3Fwl6zVcNvpp3BVtCSxfa94F8+1iMfgn2tm+kEMqIhBxSt537XnIF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\nExMTExON8f8BkD7zERI5T6wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5f90b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tifffile as tiff\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# path setting\n",
    "# 完整流程\n",
    "path_15 = 'output/tiny_15.tif'\n",
    "path_17 = 'output/tiny_17.tif'\n",
    "\n",
    "# 不做2步中的闭操作和开操作\n",
    "# path_15 = 'output/tiny_15.tif'\n",
    "# path_17 = 'output/tiny_17.tif'\n",
    "\n",
    "path_save = 'output/tiny_close_open_mius_1517.tif'\n",
    "\n",
    "img_15 = tiff.imread(path_15)\n",
    "img_17 = tiff.imread(path_17)\n",
    "\n",
    "img_minus = img_17>img_15\n",
    "\n",
    "# 格式转换和图片保存\n",
    "img_minus = np.array(img_minus, np.uint8)\n",
    "tiff.imsave(path_save, img_minus)\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.imshow(img_minus)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1517图像做差后减去河道"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/7vWYPBp110AiU+xZbuhBUyYNfcZCz+79tQ97L6gcXXM9QhJdx0JAvvnQW08c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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x64d1310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "path = 'output/tiny_close_open_mius_1517.tif'\n",
    "# path_river = 'output/channel4_threshold.tif'\n",
    "path_river = 'output/channel24_NDWI_river.tif'\n",
    "\n",
    "path_save = 'output/tiny_mius_river_1517.tif'\n",
    "\n",
    "img = tiff.imread(path)\n",
    "img_river = tiff.imread(path_river)\n",
    "\n",
    "img_minus = img>img_river\n",
    "\n",
    "img_minus = np.array(img_minus, np.uint8)\n",
    "\n",
    "tiff.imsave(path_save, img_minus)\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.imshow(img_minus)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. 对新增建筑的标注图片进行开操作减少噪声"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JtwpdlvYE+dq5rnfZTzltzSXVo2slo2RerlMOeiBlPPYkf4jUvVtbh3YepszbVA/YxwE8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\nDQaDwWAwGFTm/wNvCpF8hyNI6QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x64d2cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tifffile as tiff\n",
    "import cv2\n",
    "\n",
    "# path setting\n",
    "path = 'output/tiny_mius_river_1517.tif'\n",
    "path_save = 'output/tiny_mius_river_open_1517.tif'\n",
    "\n",
    "img = tiff.imread(path)\n",
    "\n",
    "open_kernel = np.ones((5, 5), dtype=np.uint8)\n",
    "img_open = cv2.morphologyEx(img, cv2.MORPH_OPEN, open_kernel)\n",
    "\n",
    "# 格式转换和图片保存\n",
    "img_open = np.array(img_open, np.uint8)\n",
    "tiff.imsave(path_save, img_open)\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.imshow(img_open)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.000303249520383\n"
     ]
    }
   ],
   "source": [
    "print(img_open.sum().astype(float)/255/img_pred_15.shape[0]/img_pred_15.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. 和tiny_sample进行合并提高准确率"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 官方给的例子图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(20, 20))\n",
    "plt.imshow(tiny_sample[:,:,0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. 查看结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JtwpdlvYE+dq5rnfZTzltzSXVo2slo2RerlMOeiBlPPYkf4jUvVtbh3YepszbVA/YxwE8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\nDQaDwWAwGFTm/wNvCpF8hyNI6QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4c4e210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tifffile as tiff\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "%matplotlib inline\n",
    "\n",
    "path = 'output/tiny_mius_river_open_1517.tif'\n",
    "# file_path = 'output/tiny_and_sample_1517.tif'\n",
    "\n",
    "submission = tiff.imread(path)\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.imshow(submission)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.000303249520383\n"
     ]
    }
   ],
   "source": [
    "print(submission.sum().astype(float)/255/img_pred_15.shape[0]/img_pred_15.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'numpy.ndarray'>\n",
      "(4000, 15106)\n",
      "[0 1]\n",
      "uint8\n"
     ]
    }
   ],
   "source": [
    "print(type(submission))\n",
    "print(submission.shape)\n",
    "print(np.unique(submission))\n",
    "print(submission.dtype)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
